Health-related quality of life by disease and socio-economic group in the general population in Sweden
Introduction
Measuring health-related quality of life (HRQoL) may be useful when discussing policies for improving health and reducing inequalities in health. Hence, both the average level of population health status and its distribution may be of interest.
A number of instruments have been developed to measure health-related quality of life and these instruments can be divided into general and disease-specific. A drawback of these instruments is that they describe health status on a number of different dimensions. If health status improves on one dimension and deteriorates on the other, it is difficult to know whether the overall health status has improved or not. To evaluate the overall effect of treatments, it would be useful to have an overall cardinal measure of health status. Such a measure has been developed in the field of economic evaluation of health care. In the economic evaluation field, it is common to use quality-adjusted life years (QALYs) as the outcome measure to assess the effectiveness of medical treatments. QALYs are constructed by measuring health-related quality of life on a cardinal scale between 0 (death) and 1 (full health) [1], [2]. There are three main methods of measuring these health-state utilities or quality of life (QoL) weights of different health states — the rating scale (RS), the time trade-off (TTO) and the standard gamble (SG) method [1].
One generic health-related quality of life measure is the EQ-5D self-report questionnaire [3], [4]. In the EQ-5D, health status is divided into five dimensions (mobility, self-care, usual activities, pain/discomfort and anxiety/depression) with three levels of severity in each dimension (no problems, moderate problems, and severe problems) [3]. Recently a so-called social tariff was presented that generates a single index value (mean time trade-off scores) for all the hypothetical health states described by the EQ-5D [5]. The tariff was based on a large interview study in the UK [6], [7].
Studies measuring health-related quality of life, using quality of life instruments or QoL weights, have been carried out mostly in different patient groups but also at the general population level [8], [9]. At the general population level, measures of health-related quality of life may enable comparisons of quality of life over time and comparisons of quality of life between different groups in the population, e.g. different socio-economic groups.
The EQ-5D has mostly been used in specific groups, but also in the general population in The Netherlands, Sweden, Norway, Finland, UK, Spain, US, Germany, Canada and Japan [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21]. Some of the previous studies have collected valuations from the general population for different health states defined by the EQ-5D, while others have measured health status in different groups.
For a smaller sub-set of EQ-5D health states rating scale scores have been obtained from the Swedish general population [21]. Recently, health-state utilities or QoL weights have been estimated in the general population in a county in Sweden, using the SF-12 instrument, a time trade-off question and a rating scale question [22], [23].
In 1974 Statistics, Sweden started annual surveys of living conditions aiming to provide an accurate basis for public debate about the welfare system and socio-political reforms [24]. Personal interviews have been carried out with a representative sample of about 6000 individuals annually. Questions are asked on health, economy, employment and work environment, education, housing, leisure and social relations, political activity, safety, security, and transportation. The years 1980–1981, 1988–1989, and 1996–1997 focused on questions in the health domain. Hence, these data provide a comprehensive and useful empirical material, representative of the Swedish adult population and of many sub-groups of the population, with series of data since the 1970s. Many countries have series of data from health interview surveys from the 1970s and onwards, but there are few corresponding examples of comprehensive, nation-wide surveys on health-related quality of life measures over time. If the health status data in the survey of living conditions could be converted into health-state utilities or QoL weights, it would provide a useful data set to study socio-economic differences in health status in Sweden. It would, furthermore, enable comparisons of health-related quality of life over time in Sweden.
The aim of this study is to investigate the feasibility of obtaining mean QoL weights by mapping survey data from the Swedish 1996–1997 Survey of Living Conditions to the generic health-related quality of life measure EQ-5D and to describe the health-related quality of life in terms of mean QoL weights in certain disease and socio-economic groups.
Section snippets
Material
Data from the 1996–1997 Survey of Living Conditions (ULF), a cross-sectional study based on personal interviews with a representative sample (n=11 698, aged 16–84) of the Swedish population were used. The non-response rate was about 21% [24]. Of the non-response rate, 10% was caused by sickness. Long-standing illness was reported by 45% of the respondents. The classification of the respondents self-reported long-standing illness was based on the ICD-9 classification [25]. Respondents were
Results
The predicted mean TTO QoL weight for the whole sample was 0.83. For men, the mean QoL weight was 0.86 and for women 0.81.
Discussion
We have attempted to describe and value health-related quality of life in the general Swedish population in the years 1996–1997 by disease and socio-economic group, by age and sex expressed in modified EQ-5D dimensions and in predicted mean TTO QoL weights. Mapping population health survey data to a generic health measurement seems to be a feasible way to describe and value the health status of populations.
The most appropriate methodology to test the validity of our method of mapping would have
Acknowledgments
This study was funded by Stockholm county council. All helpful comments and suggestions on earlier versions of this paper, from the Social Epidemiology, and Health Policy Research Group at the Department of Public Health Sciences, Division of Social Medicine, Karolinska Institutet and at the Nordic Health Economists’ Study Group Meeting in Reykjavik, August 1999, are acknowledged gratefully.
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